METDrive: 時系列ガイダンスを用いたマルチモーダル端到端自動運転
METDrive: Multi-modal End-to-end Autonomous Driving with Temporal Guidance
自車状態の時系列特徴を活用した時間的ガイダンスにより、知覚データの幾何特徴と統合してウェイポイント予測を行う端到端自動運転システムを提案し、CARLAベンチマークで評価した。
著者: Ziang Guo, Xinhao Lin, Zakhar Yagudin, Artem Lykov, Yong Wang, Yanqiang Li, Dzmitry Tsetserukou
分類: cs.RO, cs.CV
原文アブストラクト
Multi-modal end-to-end autonomous driving has shown promising advancements in recent work. By embedding more modalities into end-to-end networks, the system's understanding of both static and dynamic aspects of the driving environment is enhanced, thereby improving the safety of autonomous driving. In this paper, we introduce METDrive, an end-to-end system that leverages temporal guidance from the embedded time series features of ego states, including rotation angles, steering, throttle signals, and waypoint vectors. The geometric features derived from perception sensor data and the time series features of ego state data jointly guide the waypoint prediction with the proposed temporal guidance loss function. We evaluated METDrive on the CARLA leaderboard benchmarks, achieving a driving score of 70%, a route completion score of 94%, and an infraction score of 0.78.
